Hi all,
I’m looking to connect with data engineers who might be excited and interested in tackling some foundational problems related to how we integrate geospatial data into state-of-the-art data driven ML models for weather and climate. Our group is thinking about this problem very broadly, including topics related to what sort of data/datasets would be best for use with these classes of ML models, how it ought to be structured and formatted, and how we optimally incorporate it into real-world ML training and operational inference workflows.
We’d love to work with anyone interested in either the “pure” data engineering side of this (preparing and managing ML-ready archives of data for weather modeling applications) or the more ML-oriented side (optimally feeding these data into models) - or both! - and would be open to either part- or full-time commitments. Our fully remote-team is mission-oriented around the goal of “responsibly providing advanced Earth System AI to improve weather and climate-related decisions for the long-term benefit of humanity and the Earth.”
Happy to chat more with anyone interested - or if you have colleagues who might be interested in trying something new with a world-class science/engineering team, please have them reach out to daniel [at] danielrothenberg [dot] com.